The Multiple-Input Multiple-Output (MIMO) technology improves the communication system capacity and the spectrum efficiency several times over by using a space-time signal processing technology and a spatial freedom degree generated by configuring a plurality of antennas at receiving and transmitting ends, without increasing the bandwidth and the antenna transmission power. The Smart Antennas (SA) technology can generate a space directional beam by using a digital signal processing technology, so as to effectively suppress an interference signal, and greatly improve the spectrum efficiency and the channel capacity. The beamforming technology can perform a signal preprocessing by weighting the antenna array according to the channel characteristics of the user, and has the ability of extending the coverage, improving the system capacity and reducing the interference. The multi-antenna multi-stream beamforming technology combines the MIMO with the SA technology, so as to efficiently utilize airspace resources, and realize space multiplexing by transmitting a plurality of beamforming data streams simultaneously without increasing the power or sacrificing the bandwidth, thereby improving the channel capacity of the wireless communication system, and achieving high-speed and reliable information transmission.
The single-user multi-stream beamforming technology enables a single user to perform transmission of multiple data streams at certain timing, and obtain a beamforming gain and a spatial multiplexing gain, thereby achieving a higher transmission rate than the conventional single-stream beamforming technology. The conventional double-stream beamforming technology only supports two data streams, and when the adopted algorithms such as Singular Value Decomposition (SVD) based on channel decomposition are applied from the double-stream to the multi-stream, the system performance will be largely degraded under the influence of the effective eigenvalue of the channel. Therefore, the algorithm requirement of the multi-stream beamforming is quite different from that of the double-stream beamforming.
As compared with the single-user multi-stream beamforming technology, the multi-user multi-stream beamforming technology brings a higher total system capacity by using the multi-user diversity effect, and also achieves the simultaneous transmission of more user data streams. But the multi-user causes new problems such as inter-user interference. In the conventional multi-user MIMO technology based on block diagonalization and Signal to Leakage Noise Ratio (SLNR) algorithm, all users in the system shall be processed, and the execution of the algorithm requires many matrix inversions or iterations, so the complexity is very high. The primary problem to be solved in the multi-user system is to suppress the multi-user interference in the MIMO channel, and the increase of the user number makes the channel state be more complex and requires a larger overhead of the feedback channel, so it is important to design a more effective multi-user multi-stream beamforming algorithm.
The SLNR-based algorithm proposed by Mirette Sade, et al. expects the received signal power of each user to be calculated to be as large as possible, while the sum of the noise power and the interferences power leaked on other users to be as small as possible, thereby solving the inter-user signal interference, and slowing the mutual interference between the intra-user data streams. The advantage is that the target function tactfully avoids the nesting of the transmitting end weighting matrix among the users, thereby an optimized closed solution being deduced directly. In addition, the solution also breaks the antenna restrictive conditions and has a wider application space.
But during the study of the present invention, the inventor of the present invention finds that the SLNR-based algorithm proposed by Mirette Sade, et al. is just for a single user, without considering the average SLNR of the whole system. Thus, the disadvantage is that the performance of the whole system cannot be optimized under some conditions. In addition, in case that the Signal to Noise Ratio (SNR) of the system decreases, the throughout of the whole system cannot be improved even using the algorithm proposed by Mirette Sade, etc. Moreover, the conventional method does not consider a matching between the precoding vector of the transmitting end and the actually receiving processing vector.